mirror of
https://github.com/snailyp/gemini-balance.git
synced 2026-05-31 05:09:46 +08:00
290 lines
12 KiB
Python
290 lines
12 KiB
Python
import httpx
|
|
import json
|
|
import time
|
|
import uuid
|
|
import logging
|
|
from typing import Dict, Any, Optional, AsyncGenerator, Union
|
|
import openai
|
|
from app.core.config import settings
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class ChatService:
|
|
def __init__(self, base_url: str, key_manager=None):
|
|
self.base_url = base_url
|
|
self.key_manager = key_manager
|
|
|
|
def convert_messages_to_gemini_format(self, messages: list) -> list:
|
|
"""Convert OpenAI message format to Gemini format"""
|
|
converted_messages = []
|
|
for msg in messages:
|
|
role = "user" if msg["role"] == "user" else "model"
|
|
parts = []
|
|
|
|
# 处理文本内容
|
|
if isinstance(msg["content"], str):
|
|
parts.append({"text": msg["content"]})
|
|
# 处理包含图片的消息
|
|
elif isinstance(msg["content"], list):
|
|
for content in msg["content"]:
|
|
if isinstance(content, str):
|
|
parts.append({"text": content})
|
|
elif isinstance(content, dict) and content["type"] == "text":
|
|
parts.append({"text": content["text"]})
|
|
elif isinstance(content, dict) and content["type"] == "image_url":
|
|
# 处理图片URL
|
|
image_url = content["image_url"]["url"]
|
|
if image_url.startswith("data:image"):
|
|
# 处理base64图片
|
|
parts.append(
|
|
{
|
|
"inline_data": {
|
|
"mime_type": "image/jpeg",
|
|
"data": image_url.split(",")[1],
|
|
}
|
|
}
|
|
)
|
|
else:
|
|
# 处理普通URL图片
|
|
parts.append(
|
|
{
|
|
"inline_data": {
|
|
"mime_type": "image/jpeg",
|
|
"data": image_url,
|
|
}
|
|
}
|
|
)
|
|
|
|
converted_messages.append({"role": role, "parts": parts})
|
|
|
|
return converted_messages
|
|
|
|
def convert_gemini_response_to_openai(
|
|
self, response: Dict[str, Any], model: str, stream: bool = False, finish_reason: str = None
|
|
) -> Optional[Dict[str, Any]]:
|
|
"""Convert Gemini response to OpenAI format"""
|
|
if stream:
|
|
try:
|
|
if response.get("candidates"):
|
|
candidate = response["candidates"][0]
|
|
content = candidate.get("content", {})
|
|
parts = content.get("parts", [])
|
|
|
|
if "text" in parts[0]:
|
|
text = parts[0].get("text")
|
|
elif "executableCode" in parts[0]:
|
|
text = self.format_code_block(parts[0]["executableCode"])
|
|
elif "codeExecution" in parts[0]:
|
|
text = self.format_code_block(parts[0]["codeExecution"])
|
|
elif "executableCodeResult" in parts[0]:
|
|
text = self.format_execution_result(parts[0]["executableCodeResult"])
|
|
elif "codeExecutionResult" in parts[0]:
|
|
text = self.format_execution_result(parts[0]["codeExecutionResult"])
|
|
else:
|
|
text = ""
|
|
else:
|
|
text = ""
|
|
|
|
return {
|
|
"id": f"chatcmpl-{uuid.uuid4()}",
|
|
"object": "chat.completion.chunk",
|
|
"created": int(time.time()),
|
|
"model": model,
|
|
"choices": [
|
|
{
|
|
"index": 0,
|
|
"delta": {"content": text} if text else {},
|
|
"finish_reason": finish_reason,
|
|
}
|
|
],
|
|
}
|
|
except Exception as e:
|
|
logger.error(f"Error converting Gemini response: {str(e)}")
|
|
logger.debug(f"Raw response: {response}")
|
|
return None
|
|
else:
|
|
return {
|
|
"id": f"chatcmpl-{uuid.uuid4()}",
|
|
"object": "chat.completion",
|
|
"created": int(time.time()),
|
|
"model": model,
|
|
"choices": [
|
|
{
|
|
"index": 0,
|
|
"message": {
|
|
"role": "assistant",
|
|
"content": response["candidates"][0]["content"]["parts"][0][
|
|
"text"
|
|
],
|
|
},
|
|
"finish_reason": finish_reason,
|
|
}
|
|
],
|
|
"usage": {
|
|
"prompt_tokens": 0,
|
|
"completion_tokens": 0,
|
|
"total_tokens": 0,
|
|
},
|
|
}
|
|
|
|
async def create_chat_completion(
|
|
self,
|
|
messages: list,
|
|
model: str,
|
|
temperature: float,
|
|
stream: bool,
|
|
api_key: str,
|
|
tools: Optional[list] = None,
|
|
tool_choice: Optional[str] = None,
|
|
) -> Union[Dict[str, Any], AsyncGenerator[str, None]]:
|
|
"""Create chat completion using either Gemini or OpenAI API"""
|
|
|
|
if tools is None:
|
|
tools = []
|
|
if settings.TOOLS_CODE_EXECUTION_ENABLED and not model.endswith("-search"):
|
|
tools.append({"code_execution": {}})
|
|
if model.endswith("-search"):
|
|
tools.append({"googleSearch": {}})
|
|
return await self._gemini_chat_completion(
|
|
messages, model, temperature, stream, api_key, tools
|
|
)
|
|
# else:
|
|
# return await self._openai_chat_completion(
|
|
# messages, model, temperature, stream, api_key, tools
|
|
# )
|
|
|
|
async def _gemini_chat_completion(
|
|
self,
|
|
messages: list,
|
|
model: str,
|
|
temperature: float,
|
|
stream: bool,
|
|
api_key: str,
|
|
tools: Optional[list] = None,
|
|
) -> Union[Dict[str, Any], AsyncGenerator[str, None]]:
|
|
"""Handle Gemini API chat completion"""
|
|
if model.endswith("-search"):
|
|
gemini_model = model[:-7] # Remove -search suffix
|
|
else:
|
|
gemini_model = model
|
|
gemini_messages = self.convert_messages_to_gemini_format(messages)
|
|
|
|
payload = {
|
|
"contents": gemini_messages,
|
|
"generationConfig": {"temperature": temperature},
|
|
"tools": tools,
|
|
}
|
|
|
|
if stream:
|
|
async def generate():
|
|
retries = 0
|
|
MAX_RETRIES = 3
|
|
current_api_key = api_key
|
|
|
|
while retries < MAX_RETRIES:
|
|
try:
|
|
async with httpx.AsyncClient() as client:
|
|
stream_url = f"https://generativelanguage.googleapis.com/v1beta/models/{gemini_model}:streamGenerateContent?alt=sse&key={current_api_key}"
|
|
async with client.stream("POST", stream_url, json=payload) as response:
|
|
if response.status_code != 200:
|
|
error_msg = await response.text()
|
|
logger.error(f"API error: {response.status_code}, {error_msg}")
|
|
if retries < MAX_RETRIES - 1:
|
|
current_api_key = await self.key_manager.handle_api_failure(current_api_key)
|
|
retries += 1
|
|
continue
|
|
else:
|
|
logger.error(
|
|
f"Max retries reached. Final error: {response.status_code}"
|
|
)
|
|
yield f"data: {json.dumps({'error': f'API error: {response.status_code}'})}\n\n"
|
|
return
|
|
|
|
async for line in response.aiter_lines():
|
|
if line.startswith("data: "):
|
|
try:
|
|
chunk = json.loads(line[6:])
|
|
openai_chunk = self.convert_gemini_response_to_openai(
|
|
chunk, model, stream=True, finish_reason=None
|
|
)
|
|
if openai_chunk:
|
|
yield f"data: {json.dumps(openai_chunk)}\n\n"
|
|
except json.JSONDecodeError:
|
|
continue
|
|
yield f"data: {json.dumps(self.convert_gemini_response_to_openai({}, model,stream=True, finish_reason='stop'))}\n\n"
|
|
yield "data: [DONE]\n\n"
|
|
return
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Stream error: {str(e)}, attempting retry {retries + 1}")
|
|
if retries < MAX_RETRIES - 1:
|
|
current_api_key = await self.key_manager.handle_api_failure(current_api_key)
|
|
retries += 1
|
|
continue
|
|
else:
|
|
logger.error(f"Max retries reached. Final error: {str(e)}")
|
|
yield f"data: {json.dumps({'error': str(e)})}\n\n"
|
|
return
|
|
|
|
return generate()
|
|
else:
|
|
try:
|
|
async with httpx.AsyncClient() as client:
|
|
url = f"https://generativelanguage.googleapis.com/v1beta/models/{gemini_model}:generateContent?key={api_key}"
|
|
response = await client.post(url, json=payload)
|
|
if response.status_code != 200:
|
|
raise Exception(f"API error: {response.status_code}")
|
|
gemini_response = response.json()
|
|
return self.convert_gemini_response_to_openai(gemini_response, model, finish_reason="stop")
|
|
except Exception as e:
|
|
logger.error(f"Error in non-stream completion: {str(e)}")
|
|
raise
|
|
|
|
async def _openai_chat_completion(
|
|
self,
|
|
messages: list,
|
|
model: str,
|
|
temperature: float,
|
|
stream: bool,
|
|
api_key: str,
|
|
tools: Optional[list] = None,
|
|
) -> Union[Dict[str, Any], AsyncGenerator[str, None]]:
|
|
"""Handle OpenAI API chat completion"""
|
|
client = openai.OpenAI(api_key=api_key, base_url=self.base_url)
|
|
if tools:
|
|
response = client.chat.completions.create(
|
|
model=model,
|
|
messages=messages,
|
|
temperature=temperature,
|
|
stream=stream,
|
|
tools=tools,
|
|
)
|
|
else:
|
|
response = client.chat.completions.create(
|
|
model=model, messages=messages, temperature=temperature, stream=stream
|
|
)
|
|
|
|
if stream:
|
|
|
|
async def generate():
|
|
for chunk in response:
|
|
yield f"data: {chunk.model_dump_json()}\n\n"
|
|
|
|
return generate()
|
|
|
|
return response
|
|
|
|
def format_code_block(self, code_data: dict) -> str:
|
|
"""格式化代码块输出"""
|
|
language = code_data.get("language", "").lower()
|
|
code = code_data.get("code", "").strip()
|
|
|
|
return f"""\n```{language}\n{code}\n```\n"""
|
|
|
|
def format_execution_result(self, result_data: dict) -> str:
|
|
"""格式化执行结果输出"""
|
|
outcome = result_data.get("outcome", "")
|
|
output = result_data.get("output", "").strip()
|
|
return f"""\n【执行结果】\noutcome: {outcome}\noutput: {output}\n"""
|